code_test/configs/foveabox

• FoveaBox: Beyond Anchor-based Object Detector
• • Main Results
  • • Results on R50/101-FPN
  • Citations

FoveaBox: Beyond Anchor-based Object Detector

FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper https://arxiv.org/abs/1904.03797:
Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object.

Main Results

Results on R50/101-FPN

Backbone	Style	align	ms-train	Lr schd	Mem (GB)	Inf time (fps)	box AP	Config	Download
R-50	pytorch	N	N	1x	5.6	24.1	36.5	config	model | log
R-50	pytorch	N	N	2x	5.6	-	37.2	config	model | log
R-50	pytorch	Y	N	2x	8.1	19.4	37.9	config	model | log
R-50	pytorch	Y	Y	2x	8.1	18.3	40.4	config	model | log
R-101	pytorch	N	N	1x	9.2	17.4	38.6	config	model | log
R-101	pytorch	N	N	2x	11.7	-	40.0	config	model | log
R-101	pytorch	Y	N	2x	11.7	14.7	40.0	config	model | log
R-101	pytorch	Y	Y	2x	11.7	14.7	42.0	config	model | log

[1] 1x and 2x mean the model is trained for 12 and 24 epochs, respectively.
[2] Align means utilizing deformable convolution to align the cls branch.
[3] All results are obtained with a single model and without any test time data augmentation.
[4] We use 4 GPUs for training.

Any pull requests or issues are welcome.

Citations

Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.

@article{kong2019foveabox,
  title={FoveaBox: Beyond Anchor-based Object Detector},
  author={Kong, Tao and Sun, Fuchun and Liu, Huaping and Jiang, Yuning and Shi, Jianbo},
  journal={arXiv preprint arXiv:1904.03797},
  year={2019}
}